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--- |
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license: mit |
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base_model: facebook/xlm-v-base |
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tags: |
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- generated_from_trainer |
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metrics: |
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- accuracy |
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- f1 |
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model-index: |
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- name: scenario-TCR-XLMV-XCOPA-5_data-xcopa_all |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# scenario-TCR-XLMV-XCOPA-5_data-xcopa_all |
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This model is a fine-tuned version of [facebook/xlm-v-base](https://huggingface.co/facebook/xlm-v-base) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.6931 |
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- Accuracy: 0.5125 |
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- F1: 0.4891 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 5e-05 |
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- train_batch_size: 32 |
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- eval_batch_size: 32 |
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- seed: 214 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- num_epochs: 500 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | |
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|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:| |
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| No log | 0.38 | 5 | 0.6925 | 0.5333 | 0.5246 | |
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| No log | 0.77 | 10 | 0.6931 | 0.4867 | 0.4680 | |
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| No log | 1.15 | 15 | 0.6932 | 0.475 | 0.4483 | |
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| No log | 1.54 | 20 | 0.6932 | 0.4658 | 0.4241 | |
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| No log | 1.92 | 25 | 0.6932 | 0.4975 | 0.4724 | |
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| No log | 2.31 | 30 | 0.6931 | 0.5175 | 0.4970 | |
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| No log | 2.69 | 35 | 0.6931 | 0.5142 | 0.4987 | |
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| No log | 3.08 | 40 | 0.6931 | 0.5167 | 0.5026 | |
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| No log | 3.46 | 45 | 0.6931 | 0.53 | 0.5146 | |
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| No log | 3.85 | 50 | 0.6931 | 0.5492 | 0.5292 | |
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| No log | 4.23 | 55 | 0.6931 | 0.5517 | 0.5394 | |
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| No log | 4.62 | 60 | 0.6931 | 0.5508 | 0.5357 | |
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| No log | 5.0 | 65 | 0.6931 | 0.5533 | 0.5387 | |
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| No log | 5.38 | 70 | 0.6930 | 0.5592 | 0.5428 | |
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| No log | 5.77 | 75 | 0.6930 | 0.5608 | 0.5453 | |
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| No log | 6.15 | 80 | 0.6931 | 0.54 | 0.5258 | |
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| No log | 6.54 | 85 | 0.6933 | 0.4958 | 0.4860 | |
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| No log | 6.92 | 90 | 0.6931 | 0.5308 | 0.4987 | |
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| No log | 7.31 | 95 | 0.6931 | 0.53 | 0.5130 | |
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| No log | 7.69 | 100 | 0.6931 | 0.5292 | 0.5074 | |
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| No log | 8.08 | 105 | 0.6931 | 0.5358 | 0.5101 | |
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| No log | 8.46 | 110 | 0.6931 | 0.5225 | 0.4943 | |
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| No log | 8.85 | 115 | 0.6925 | 0.5575 | 0.5354 | |
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| No log | 9.23 | 120 | 0.6931 | 0.5417 | 0.5250 | |
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| No log | 9.62 | 125 | 0.6931 | 0.5133 | 0.4804 | |
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| No log | 10.0 | 130 | 0.6931 | 0.5358 | 0.5004 | |
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| No log | 10.38 | 135 | 0.6931 | 0.5425 | 0.5163 | |
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| No log | 10.77 | 140 | 0.6931 | 0.5433 | 0.5142 | |
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| No log | 11.15 | 145 | 0.6931 | 0.5425 | 0.5103 | |
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| No log | 11.54 | 150 | 0.6931 | 0.5467 | 0.5099 | |
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| No log | 11.92 | 155 | 0.6931 | 0.5358 | 0.4986 | |
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| No log | 12.31 | 160 | 0.6931 | 0.5275 | 0.4841 | |
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| No log | 12.69 | 165 | 0.6931 | 0.5192 | 0.4825 | |
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| No log | 13.08 | 170 | 0.6931 | 0.5283 | 0.4910 | |
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| No log | 13.46 | 175 | 0.6930 | 0.5508 | 0.5131 | |
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| No log | 13.85 | 180 | 0.6930 | 0.5542 | 0.5303 | |
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| No log | 14.23 | 185 | 0.6932 | 0.4908 | 0.4664 | |
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| No log | 14.62 | 190 | 0.6931 | 0.5075 | 0.4802 | |
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| No log | 15.0 | 195 | 0.6932 | 0.5083 | 0.4806 | |
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| No log | 15.38 | 200 | 0.6932 | 0.4625 | 0.4377 | |
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| No log | 15.77 | 205 | 0.6932 | 0.48 | 0.4694 | |
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| No log | 16.15 | 210 | 0.6932 | 0.49 | 0.4742 | |
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| No log | 16.54 | 215 | 0.6931 | 0.5333 | 0.5130 | |
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| No log | 16.92 | 220 | 0.6931 | 0.5217 | 0.4884 | |
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| No log | 17.31 | 225 | 0.6931 | 0.5125 | 0.4891 | |
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### Framework versions |
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- Transformers 4.33.3 |
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- Pytorch 2.1.1+cu121 |
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- Datasets 2.14.5 |
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- Tokenizers 0.13.3 |
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